code: def _plot_roc(fpr, tpr, ax, label=None):
      """
      Plot ROC curve
  
      Parameters
      ----------
      fpr : ndarray of shape (>2,)
          Increasing false positive rates such that element i is the false
          positive rate of predictions with score >= `thresholds[i]`
  
      tpr : ndarray of shape (>2,)
          Increasing true positive rates such that element `i` is the true
          positive rate of predictions with score >= `thresholds[i]`
  
      ax: matplotlib Axes
          Axes object to draw the plot onto
  
      Returns
      -------
      ax: matplotlib Axes
          Axes containing the plot
  
      Notes
      -----
      .. versionadded:: 0.8.4
      """
      roc_auc = auc(fpr, tpr)
  
      label = label or "ROC curve"
  
      ax.plot(fpr, tpr, label=(f"{label} (area = {roc_auc:0.2f})"))
  
      _set_ax_settings(ax)
      return ax
  
imports: import numpy
  import matplotlib.pyplot
  from sklearn.metrics import roc_curve, auc
  from sklearn.preprocessing import label_binarize
  from ..telemetry import SKLearnEvaluationLogger
  from ..util import is_column_vector, is_row_vector
  from sklearn_evaluation import __version__
  import json
  from pathlib import Path
  from sklearn_evaluation.plot.plot import AbstractPlot, AbstractComposedPlot
  from ploomber_core.exceptions import modify_exceptions
label_desc: Use image node_id 4 for calling a global function with example usage: _plot_roc(fpr, tpr, ax, label) and returns: ax